Andrew Critch
Impact in
- Computational Mathematics top 10%
- Tensor decomposition and applications
Papers in
-
- Bayesian Modeling and Causal Inference 2
- Reinforcement Learning in Robotics 2
- Logic, Reasoning, and Knowledge 2
- Neural Networks and Applications 2
- Co-authors
- Mario Fedrizzi (2 shared papers)Matteo Brunelli (1 shared paper)Karl Swanson (1 shared paper)Danielle S. Bitterman (1 shared paper)Stuart Russell (4 shared papers)David Chen (1 shared paper)Fei‐Fei Liu (1 shared paper)Srinivas Raman (1 shared paper)
- Journals
- Applied Mathematics and Computation (1 paper)Decisions in Economics and Finance (1 paper)Journal of Symbolic Logic (1 paper)Symmetry Integrability and Geometry Methods and Applications (1 paper)PubMed (1 paper)
- Partner nations
- United StatesItalyFinland
In The Last Decade
Andrew Critch
11 papers receiving 75 citations
Peers
Comparison fields: 5 of 45
- Computational Mathematics 14
- Health Informatics 3
- Management Science and Operations Research 28
- General Decision Sciences 2
- Computational Theory and Mathematics 17
Countries citing papers authored by Andrew Critch
This map shows the geographic impact of Andrew Critch's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Andrew Critch with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Andrew Critch more than expected).
Fields of papers citing papers by Andrew Critch
This network shows the impact of papers produced by Andrew Critch. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Andrew Critch. The network helps show where Andrew Critch may publish in the future.
Co-authors
The 15 scholars most cited alongside Andrew Critch, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2013 | 28 | |
| 2 | 2014 | 13 | |
| 3 | 2025 | 12 | |
| 4 | 2020 | 9 | |
| 5 | Algebraic Geometry of Hidden Markov and Related Models | 2013 | 5 |
| 6 | Neural Networks are Surprisingly Modular | 2020 | 3 |
| 7 | 2020 | 2 | |
| 8 | 2017 | 2 | |
| 9 | Negotiable Reinforcement Learning for Pareto Optimal Sequential Decision-Making | 2018 | 1 |
| 10 | 2019 | 1 | |
| 11 | The MAGICAL Benchmark for Robust Imitation | 2020 | 1 |
| 12 | Importance and Coherence: Methods for Evaluating Modularity in Neural Networks | 2021 | 0 |
About Andrew Critch
Andrew Critch is a scholar working on Artificial Intelligence, Computational Theory and Mathematics, Management Science and Operations Research, Biophysics and Statistics and Probability, having authored 12 papers that have together received 77 indexed citations. Recurring topics across this work include Multi-Criteria Decision Making (2 papers), Cell Image Analysis Techniques (2 papers), Bayesian Modeling and Causal Inference (2 papers), Reinforcement Learning in Robotics (2 papers), Logic, Reasoning, and Knowledge (2 papers), Neural Networks and Applications (2 papers), Complex Systems and Decision Making (1 paper) and Fuzzy Systems and Optimization (1 paper). The work is most often cited by research in Computational Mathematics (14 citations), Health Informatics (3 citations), Management Science and Operations Research (28 citations), General Decision Sciences (2 citations) and Computational Theory and Mathematics (17 citations). Andrew Critch has collaborated with scholars based in United States, Italy and Finland. Frequent co-authors include Mario Fedrizzi, Matteo Brunelli, Karl Swanson, Danielle S. Bitterman, Stuart Russell, David Chen, Fei‐Fei Liu, Srinivas Raman, Alexandre M. Bayen and Natasha Jaques. Their work appears in journals such as Applied Mathematics and Computation, Decisions in Economics and Finance, Journal of Symbolic Logic, Symmetry Integrability and Geometry Methods and Applications and PubMed.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.